Microsoft Operationalizing Machine Learning and Generative AI Solutions : AI-300

Exam Code: AI-300
Exam Name: Operationalizing Machine Learning and Generative AI Solutions
Updated: Sep 23, 2026
Q&As: 189 Questions and Answers

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Microsoft AI-300 Exam Syllabus Topics:

Section	Objectives
Topic 1: Design and implement a GenAIOps infrastructure	- Set up Microsoft Foundry and Azure AI services for generative AI workloads - Configure prompt orchestration, prompt flows, and agent frameworks - Manage API keys, rate limits, and responsible AI guardrails - Implement RAG (Retrieval-Augmented Generation) pipelines and vector search
Topic 2: Design and implement an MLOps infrastructure	- Implement security, governance, and compliance for MLOps - Manage environments, data stores, and model registries - Configure source control, CI/CD pipelines, and automation for ML workflows - Set up Azure Machine Learning workspace and compute targets
Topic 3: Implement generative AI quality assurance and observability	- Conduct red teaming, adversarial testing, and content filtering - Monitor latency, token usage, cost, and error rates - Evaluate generative AI outputs for quality, safety, and grounding - Implement logging, tracing, and telemetry for GenAI applications
Topic 4: Implement machine learning model lifecycle and operations	- Retrain, update, and manage model versions in production - Deploy models to real-time and batch endpoints - Monitor model performance, data drift, and operational health - Train, register, and version models using Azure Machine Learning
Topic 5: Optimize generative AI systems and model performance	- Optimize inference performance, caching, and throughput - Implement cost management and scaling strategies for GenAI workloads - Tune prompts, system messages, and grounding strategies - Fine-tune and distill models for specific use cases

Microsoft Operationalizing Machine Learning and Generative AI Solutions Sample Questions:

Question #1

You have a Microsoft Foundry project.
You plan to use the Microsoft Foundry portal to fine-tune a base Azure OpenAI Service model that can accept both text and images as input.
You need to choose the suitable model.
Which model should you choose?

A. davinci-002
B. gpt-35-turbo
C. gpt-4
D. gpt-4o

Reveal Solution Discussion 0

Correct Answer: D 🗳️

Explanation: Only visible for Prep4away members. You can sign-up / login (it's free).

Question #2

-
A team is developing a Retrieval-Augmented Generation (RAG) system.
The team requires improvements to the system ' s retrieval quality to ensure accurate, grounded responses.
You need to assess RAG performance before you can suggest an improvement strategy.
Which four actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.

Reveal Solution Discussion 0

Correct Answer:

Explanation:
Correct sequence:
* Collect retrieval logs.
* Run RAG evaluators.
* Adjust chunking strategy.
* Re-index documents.
The process should begin by collecting retrieval logs . Retrieval diagnostics provide the evidence needed to understand which queries were issued, which document chunks were returned, and whether relevant content is being surfaced. Without retrieval telemetry, optimization becomes speculative rather than measurable.
Next, run RAG evaluators . Microsoft Foundry provides retrieval-oriented evaluators that assess how relevant retrieved context is to a query. The Retrieval evaluator measures contextual relevance without requiring ground truth, while the Document Retrieval evaluator can calculate metrics such as Fidelity and NDCG when labeled retrieval ground truth exists. These measurements help establish the retrieval-quality baseline before modifications are made.
After identifying retrieval deficiencies, adjust the chunking strategy . Microsoft specifically recommends reviewing chunk size and chunking methodology when retrieval returns irrelevant or incomplete passages.
Chunks that are too small can lose context, while oversized chunks can introduce irrelevant material and reduce retrieval precision.
Finally, re-index the documents so the revised chunking configuration is reflected in the searchable corpus.
Changing temperature or regenerating the prompt template primarily affects generation behavior rather than correcting the underlying retrieval pipeline.
Study Guide Reference: Implement generative AI quality assurance and observability - RAG evaluation, retrieval telemetry, retrieval-quality metrics, chunk optimization, indexing, and grounded-response assessment.

Question #3

You need to recommend an experiment-tracking strategy that ensures consistent experiment results.
What should you recommend?

A. Application Insights logs
B. Azure Monitor alerts
C. Azure Machine Learning job output logs
D. MLflow experiment tracking

Reveal Solution Discussion 0

Correct Answer: D 🗳️

Explanation: Only visible for Prep4away members. You can sign-up / login (it's free).

Question #4

A financial services company is deploying Microsoft Foundry to host generative AI workloads that process regulated customer data. The Microsoft Foundry environment must prevent any public network exposure while still allowing services managed by Microsoft Foundry to communicate with dependent Azure resources.
Security auditors require that all traffic to and from the Microsoft Foundry resource remain on private networks, with no public endpoints available.
You need to configure the Microsoft Foundry environment so that network access is restricted while maintaining full platform functionality.
Which two actions should you perform? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point. Choose two.

A. Use API key authentication for all model endpoints.
B. Deploy the Microsoft Foundry resource in a separate Azure subscription.
C. Configure a managed virtual network for the Microsoft Foundry resource.
D. Disable public network access to the Microsoft Foundry resource.
E. Disable all inbound network access.

Reveal Solution Discussion 0

Correct Answer: C,E 🗳️

Explanation: Only visible for Prep4away members. You can sign-up / login (it's free).

Question #5

Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals.
You have an Azure Machine Learning workspace. You connect to a terminal session from the Notebooks page in Azure Machine Learning studio.
You plan to add a new Jupyter kernel that will be accessible from the same terminal session.
You need to perform the task that must be completed before you can add the new kernel.
Solution: Create an environment.
Does the solution meet the goal?

A. Yes
B. No

Reveal Solution Discussion 0

Correct Answer: A 🗳️

Explanation: Only visible for Prep4away members. You can sign-up / login (it's free).

Microsoft Operationalizing Machine Learning and Generative AI Solutions : AI-300

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